Changes in Loon (<em>Gavia</em> spp.) and Red-necked Grebe (<em>Podiceps grisegena</em>) Populations in the Lower Matanuska-Susitna Valley, Alaska
Bibliographic record
Abstract
More than two-thirds of the human population of Alaska resides in the south-central portion of the state, where its continued growth is likely to affect some wildlife populations negatively. To assess changes in waterbird populations in this region, we compared counts of Common Loons (Gavia immer), Pacific Loons (G. pacifica), and Red-necked Grebes (Podiceps grisegena) made on Matanuska-Susitina Valley lakes. In general, the number of lakes occupied by loon or grebe pairs decreased between 1987 and 1999. Decreases in the number of lakes occupied by Common Loons were less drastic in the northwest region of the study area than in the southeast region; human development is greater in the southeastern portion of our study area. Contrary to lake occupancy, the percentage of lakes that fledged Common Loon chicks remained stable between years. Because the human population is expected to continue to grow, proactive management of lake use and lakeshore development, coupled with monitoring of loon and grebe occupancy and productivity, is needed to ensure the persistence of these waterbird populations in the lower Matanuska-Susitna Valley.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".